Bibliographic record
Abstract
The beetle fauna of Canada was assessed, including estimates of yet unreported diversity using information from taxonomists and COI sequence clusters in a BOLD (Barcode of Life Datasystems) COI dataset comprising over 77,000 Canadian records. To date, 8302 species of Coleoptera have been recorded in Canada, a 23% increase from the first assessment in 1979. A total of 639 non-native beetle species have become established in Canada, with most species in the Staphylinidae (153 spp.), Curculionidae (107 spp.), Chrysomelidae (56 spp.) and Carabidae (55 spp.). Based on estimates from the taxonomic community and our BOLD dataset, we estimate that slightly more than 1000 beetle species remain to be reported from Canada, either as new records or undescribed species. Renewed enthusiasm toward and financial support for surveys, especially in the central and western provinces of Canada will be critical for detecting, documenting and describing these species. The Barcode of Life database is still far from comprehensive for Canadian Coleoptera but substantial progress has been made and the number of Barcode Index Numbers (BINs) (as candidate species) has reached nearly 70% of the number of species reported from Canada. Comparison of BINs to observed species in a group of Canadian Staphylinidae suggests that BINs may provide a good estimate of species diversity within the beetles. Histeridae is a diverse family in Canada that is notably underrepresented in BOLD. Families such as Mordellidae, Scraptiidae, Latridiidae, Ptiliidae and Scirtidae are poorly known taxonomically in Canada and are represented in our BOLD dataset by many more BINs than recorded species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".